Google Professional Machine Learning Engineer

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In the development of a machine learning model for a healthcare application, you are faced with the dual challenges of adhering to strict data privacy regulations and operating within a constrained computational budget. Cross-validation is a technique you consider to ensure the model's effectiveness and compliance. Which of the following statements accurately describe the benefits of cross-validation in this context? (Choose 2 options)

Real Exam

Cross-validation ensures the model will always overfit by testing on multiple subsets of data, thereby guaranteeing high accuracy on the training data.

3.7%

By systematically varying the training dataset size, cross-validation directly minimizes computational costs without affecting model performance.

7.4%

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Cross-validation evaluates the model's generalization to unseen data while maintaining data privacy, by using distinct subsets for training and validation.

46.3%

The technique prevents the model from learning any meaningful patterns by frequently altering the training dataset, ensuring compliance with privacy regulations.

3.7%

Through performance evaluation across different data splits, cross-validation aids in selecting model parameters that achieve optimal accuracy and comply with data privacy laws.

38.9%